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fmind/mlops-python-package
A comprehensive Python package template to kickstart and standardize your MLOps initiatives and data pipelines.
A Python project template for MLOps workflows, built around a bike-sharing dataset as the working example. It wires together uv, MLflow, Pydantic, Pandera, and a DAG-style job runner into a structure you can clone and adapt. Aimed at ML engineers who want a starting point with tooling already chosen and integrated.
The Pydantic discriminated union pattern for job dispatch is clean — you swap job types in YAML and the right class gets instantiated without touching code. Pandera schema definitions for DataFrames catch column/type mismatches at runtime, which is usually the first thing teams skip and later regret. The IO separation between jobs and datasets is real, not just a folder rename — readers/writers are swappable via config. The justfile task runner covers the full lifecycle (format, lint, test, build, MLflow, Docker) without requiring Make.
The DAG story is just a justfile with sequential job invocations — there's no real dependency graph, parallelism, or retry logic. If any step fails mid-pipeline you're restarting manually. The alerting choice (Plyer desktop notifications) is a toy for local dev; there's nothing here for production monitoring beyond MLflow metrics. The template is tightly coupled to scikit-learn and tabular data — anyone doing deep learning, NLP, or streaming will spend more time ripping things out than building on top. MLflow as the single observability tool means you're running a local MLflow server just to see training curves, which is real overhead for small teams that would be better served by something lighter.